论文标题
CPA：英国基于国家机器学习的医院能力计划系统
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
论文作者
论文摘要
2019年冠状病毒病（COVID-19）全球大流行构成了对重症监护资源有前所未有的需求压倒性的医疗保健系统的威胁。如果没有全国范围的集体努力，就无法有效地进行管理这些要求，该努力依靠数据来预测医院对国家，地区，医院和个人级别的需求。为此，我们开发了COVID-19-COVID-19的能力计划和分析系统（CPA） - 一种基于机器学习的医院资源计划系统，我们已成功地在各个医院和英国各地部署了与NHS Digital的协调。在本文中，我们讨论了以国家规模部署基于机器学习的决策支持系统的主要挑战，并解释了CPA如何通过（1）定义适当的学习问题来解决这些挑战，（2）将自下而上和自上而下的分析方法结合在一起，（3）使用最先进的机器学习算法，（4）将杂物性数据源组成的杂物数据源和（5）相互作用和（5）An An An an An An An and An and An and An and An。 CPA是第一个以机器学习为基础的系统之一，该系统将以全国范围的规模部署在医院中，以解决COVID-19的大流行 - 我们总结了这篇论文，总结了从这种经验中学到的经验教训。
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS) - a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic - we conclude the paper with a summary of the lessons learned from this experience.
